1. Field of the Invention
Embodiments of the present invention relate to systems and methods for hyperspectral imaging using static coded aperture spectrometers. More particularly, embodiments of the present invention relate to systems and methods in which translation, rotation, and defocus is combined with coded aperture spectrometers to implement hyperspectral imaging with high optical efficiency, with low component and design cost, and without the missing cone problem.
2. Background Information
Traditional digital imaging techniques produce images with scalar values associated with each spatial pixel location. In imaging spectroscopy, these scalar values are replaced with a vector containing the spectral information from that spatial location. The resulting datacube is, therefore, three-dimensional (two spatial dimensions and one spectral dimension).
Spectral imaging has traditionally focused on environmental remote sensing and military target recognition tasks. In recent years, however, there has been a dramatic growth in biophotonics applications, and with that growth has come an increased interest in spectral imaging for biological applications (especially those with security applications).
The number of data points, or voxels, in the hyperspectral datacube is typically large. For example, a 1000 by 1000 pixel image with 100 spectral channels corresponds to a datacube with 108 voxels. As a result of both the large number of voxels and the geometric complexity associated with sampling the three-dimensional (3D) datacube on two-dimensional (2D) optoelectronic detector arrays, it is generally necessary to scan some system parameter as a function of time or to use multiple apertures to fully characterize the datacube. Selection of a spatial, spectral, and temporal sampling strategy and integration of the sampling strategy with physical design and image estimation algorithms are the primary challenges addressed in the design of hyperspectral imaging systems.
Hyperspectral imagers may be classified as isomorphic systems, in which each detected measurement corresponds to a specific voxel in the datacube, or multiplex systems, in which each measurement corresponds to a linear combination of voxel values. Isomorphic systems have been constructed using tunable filters and filter arrays, spectrally sensitive detector arrays, and push broom dispersive spectrometers. The simplest type of isomorphic system combines a tomographic (rotational scanning) or pushbroom (linear scanning) front-end with a traditional slit-based dispersive spectrometer. Isomorphic systems are relatively simple to construct, although the numerical aperture of tunable filters is in some cases limited. The disadvantage of isomorphic systems is that photon efficiency is inversely proportional to spatiospectral resolution. Poor optical efficiency results in relatively long acquisition times and a poor signal to noise ratio (SNR).
There have been a number of ingenious solutions to the light collection problem of isomorphic systems over the years. Two very advanced solutions are the scanning-Michelson Fourier-transform spectrometers, and multiplexed pushbroom designs based on digital micro-mirror (DMM) technology. Both approaches have proven highly successful. However, they involve expensive components that are not terribly robust.
Multiplex systems have been constructed using Fourier transform interferometry, Hadamard transform dispersion, as well as spectral tomographic approaches. Multiplex data must be digitally processed to estimate the datacube. The advantage of multiplexing is that the throughput, defined as the ratio of the optical power entering the system aperture to detected power, may be as high as 50-100%. While high throughput is not a panacea, higher throughput is generally associated with better sensitivity and higher SNR.
Functional performance, resolution, system cost, computational complexity and stability, weight, volume, and many other metrics arise in comparing isomorphisms and multiplexing schemes. Previous multiplexing designs have been constrained by high component and design cost associated with interferometric stability for Fourier transform systems, the relatively complex and expensive challenges of Hadamard systems based on dynamic spatial light modulators, as well as the “missing cone” problem associated with spectral tomography. The missing cone describes the dependence of spectral resolution on image spatial frequency. As a result of the missing cone, ill-posed inference techniques are necessary to estimate the spectra of coarse features in spectral tomography images.
In view of the foregoing, it can be appreciated that a substantial need exists for systems and methods that can advantageously perform hyperspectral imaging with a high optical efficiency, with a low component and design cost, and without the missing cone problem.